Recent advancements in computational hardware and parallel processing are driving developments in metaheuristic algorithms. These advancements help solve increasingly complex real-world optimization problems. As a result, more sophisticated and computationally demanding algorithms can now be implemented. This paper presents a comparative performance evaluation of ten modern metaheuristic algorithms for the optimal design of Electromagnetic Devices (EMDs). It also evaluates seven well-established metaheuristics, which are referred to as reference algorithms. In addition to the comparison, a novel hybrid optimization strategy called Multiple Combined Algorithms for Optimization (MuCAO) is proposed. MuCAO probabilistically combines the best-performing algorithms to leverage their complementary strengths. All algorithms, including MuCAO, were tested on six benchmark problems with various complexities and design variables. These benchmarks include analytical models and problems based on the Finite Element Method (FEM). For validation, the approach was also applied to a real-life application, which is Sidelobe Level Reduction in a Circular Antenna Array (CAA). The results show that MuCAO outperformed all other algorithms and achieved the highest overall ranking. Three modern metaheuristics followed. The best-performing reference algorithm ranked lower, with DE in fifth place. The study confirms that modern metaheuristics generally offer superior performance for EMD design and optimization compared to traditional metaheuristics.
Bouchekara et al. (Mon,) studied this question.